Method for assessing equity adequacy

ABSTRACT

Risk-based method for assessing an automotive finance company&#39;s equity adequacy wherein sources of creditor protection comprises equity, reserves, net deferred tax liability in the event of an overall loss, future tax liability and lifetime profits. Potential unexpected worst-case losses for each of a plurality of exposures is estimated with 99.9% confidence and compared with the company&#39;s creditor protection to demonstrate the company&#39;s equity adequacy.

BACKGROUND OF INVENTION

The present invention relates generally to assessing a finance company'sequity adequacy and, more particularly, to a risk-based method forassessing an automotive finance company's equity adequacy.

Finance companies need to provide creditors with high confidence ofrepayment. Conventionally, financing companies assess theircapitalization merely in terms of equity and existing debt. What theconventional analysis lacks, however, is the reliable assessment ofalternate sources of creditor protection. For example, deferred taxes inthe event of an overall loss can be included as equity for creditorprotection. Similarly, future tax liability and future income arealternate sources of creditor protection not included in theconventional equity assessment.

Another shortfall of the conventional capitalization assessment is theabsence of an unexpected loss assessment at the 99.9% confidence levelfor each of the finance company's categories of risk.

Furthermore, the conventional assessment fails to correlate occurrencesof unexpected loss by falsely assuming that all unexpected losses occurat the same time. As a result, the conventional assessment quantifiespotential uses of creditor protection at a level higher thanrealistically necessary.

What is needed is a risk-based method for assessing equity adequacy thatintegrates conventional and alternate sources of creditor protection, arisk-based assessment of unexpected loss at the 99.9% confidence leveland a practical correlation between occurrences of unexpected losses.

SUMMARY OF INVENTION

A method is provided for assessing an automotive finance company'sequity adequacy. New sources of creditor protection including deferredtax liability in the event of an overall loss, future tax liability andlifetime profits are quantified and compared to an estimation of thecompany's potential unexpected worst-case losses for each of a pluralityof exposures at a 99.9% confidence level.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block flow diagram illustrating an overview of a preferredembodiment of the present invention;

FIG. 2 is a block diagram illustrating a business model for ahypothetical automotive finance company;

FIG. 3 is a block diagram illustrating the priority among stakeholdersin the finance company's capitalization;

FIG. 4 is a preferred framework for estimating unexpected losses inaccord with the present invention;

FIG. 5 is a block flow diagram illustrating a methodology for estimatingunexpected retail credit losses;

FIG. 6 is a chart illustrating a hypothetical correlation betweenhistorical financing contract defaults and the severity of thosedefaults;

FIG. 7 is a chart illustrating a region of creditor protection based onworst-case retail credit loss;

FIG. 8 is a chart illustrating unexpected retail credit losses based onthe difference between expected retail credit losses and worst-caseretail credit loss;

FIG. 9 is a block flow diagram illustrating a methodology for estimatingunexpected residual lease losses;

FIG. 10 is a chart illustrating a comparison between actual past auctionvalues as a percent of manufacturer suggested retail price (MSRP) andpast auction values simulated in accord with the present invention;

FIG. 11 is a chart illustrating the inaccuracy of an auction valueforecasting simulation;

FIG. 12 is a chart illustrating the frequency of residual lease lossesand credit lease losses as a function of loss severity;

FIG. 13 is a chart illustrating unexpected residual lease losses basedthe difference between expected residual lease losses and a simulatedworst-case residual lease loss;

FIG. 14 is a block flow diagram illustrating a methodology 147 forestimating unexpected commercial losses;

FIG. 15 is a Venn diagram illustrating the correlation of retail risk,lease risk and commercial risk in accord with the present invention;

FIG. 16 is a table illustrating an integration of correlation into ahypothetical unexpected loss calculation; and

FIG. 17 is a chart comparing sources of creditor protection to the usesof creditor protection over the lifetime of a finance company'sproducts.

DETAILED DESCRIPTION

FIG. 1 is a block flow diagram illustrating an overview of a preferredembodiment of the present invention. Generally, the risk-based equityadequacy assessment comprises the steps of identifying sources ofcreditor protection as shown in block 13, estimating unexpected lossesas shown in block 14 and comparing the sources of creditor protection tothe unexpected losses as shown in block 15.

For purposes of clarity and understanding, the remainder of the bestmode for carrying out the invention is divided into three principalsections. The first section describes the step of identifying sources ofcreditor protection in accord with the present invention. The secondsection describes estimating unexpected losses in accord with thepresent invention. The third section describes comparing the sources ofcreditor protection to the potential unexpected losses in accord withthe present invention.

Identifying Sources of Creditor Protection FIG. 2 is a block diagramillustrating a business model for a hypothetical automotive financecompany. Generally, the finance company's sources of creditor protectionare quantified on the projected income statement 25 (i.e., financingrevenue 27) and present balance sheet 26 (i.e., receivables 28).

As defined by the projected income statement 25, revenues should coverlifetime profits from the company's existing portfolio 29, future taxes30, lifetime expected losses covered by pricing 31, operating costs 32and borrowing costs 33. Pricing covers all required borrowing andoperating costs, expected losses, taxes and provides profits forcompensation to shareholders 38.

Sources of creditor protection defined by the balance sheet 26 assetsare funded with present deferred tax liability 34, present debt 35,present reserves 36 and present equity 37.

After operating costs are covered and expected losses are incurred,creditors 39 have rights to the borrowing costs 33 and debt 35. Thegovernment 40 has rights to taxes on the future earnings 30 and thepresent deferred tax liabilities 34. Shareholders 38 own future equity29, present reserves 36 and present equity 37.

FIG. 3 is a block diagram illustrating the priority among stakeholdersin the finance company's capitalization. Creditors 45 have a seniorposition relative to the government 46 and shareholders 47.

Sources of creditor protection for unexpected losses comprise projectedfuture taxes on existing business 50, present deferred tax liability 51,projected lifetime profits from the finance company's existing portfolio(i.e. future equity) 52, present reserves 53 and present equity asquantified on the company's financial statement 54.

In the event of a net loss scenario, no future taxes 50 are due to thegovernment 46. In addition, tax credits generated by the net losseliminate any present deferred tax liability 51. In accord with apreferred embodiment of the present invention, the funds that thecompany designated to pay present deferred taxes 51 and income taxes onfuture earnings 50 can be applied to service debt to creditors 45, asillustrated by arrow 55.

Estimate Potential Unexpected Losses FIG. 4 is a preferred framework forestimating unexpected losses in accord with the present invention andthe hypothetical business model illustrated in FIG. 2. In accord withthe hypothetical business model, the automotive finance companyexposures capable of generating unexpected losses comprise retail loanreceivables 60, lease assets 61, commercial loan receivables 62,securitized assets 63, foreign affiliate assets 64, miscellaneous assets65 and non-product risks 66. Unexpected credit losses 67-71 may begenerated in the retail 60, lease 61, commercial 62, securitization 63and foreign market 64 asset classes. Unexpected residual losses 72-73may be generated in the lease 61 and foreign market 64 asset classes.Unexpected miscellaneous losses 74-76 may be generated in the foreignmarket 64, miscellaneous 65 and non-product 66 asset classes.

FIG. 5 is a block flow diagram illustrating a methodology for estimatingunexpected retail credit losses 67. In accord with a preferredembodiment of the present invention, the methodology comprises compilinga historical portfolio of fully liquidated financing contracts by loanclass as illustrated in block 80, analyzing distributions andcorrelations of defaults and severities among the loan classes asillustrated in block 81, applying a retail credit loss distribution andcorrelation simulation as illustrated in block 82 and estimatingunexpected retail credit losses for the entire portfolio based on thesimulation as illustrated in block 83.

The portfolio of fully liquidated financing contracts 80 are segregatedby loan classes in terms of contract terms, risk and products. Forexample, a portfolio in the automotive industry might comprise financingcontracts varying in term (e.g., 36, 48, 60, 72 months etc.), product(e.g., brand, new, used, etc.), and tiers of risk.

Preferably, the simulation is run 200,000 times resulting in thedistribution of potential losses. From this distribution, theprobability of each level of losses occurring is estimated.

FIG. 6 is a chart illustrating a hypothetical correlation betweenhistorical financing contract defaults and the severity of thosedefaults. The X-axis 88 indicates the average loss in the event of afinancing contract default as a percentage of the amount originallyfinanced. The Y-axis 89 indicates the frequency of default as a percentof origination volume. Notably, the plotted points 90 are tightlyclustered demonstrating a low volatility and high predictability for thefinancing contract defaults. The worst-case retail credit loss 95 thatcan occur with 99.9% confidence is calculated based on the distributionof potential worst-case losses 90.

FIG. 7 is a chart illustrating a region of creditor protection 99 basedon the worst-case retail credit loss 95. All combinations of default andseverity along the creditor protection line 100 equate to the worst-caseretail credit loss 95. Notably, creditors are protected with 99.9%confidence for any outcome below the creditor protection line 100.

FIG. 8 is a chart illustrating unexpected retail credit losses based onthe difference between expected retail credit losses 105 and theworst-case retail credit loss 106 calculated according to the results ofthe simulation model.

Unexpected retail credit losses with 99.9% confidence are calculated bysubtracting the expected retail credit losses 105 from the worst-caseretail credit loss 106.

Unlike the retail asset class, the automobile leasing asset class 61 hasa relatively short history. As a result, the methodology for estimatinglease credit losses is preferably based on the historical retailfinancing contracts having a 48 month term. Notably, this methodology isconservative due to the fact that leasing contracts are conventionallyshorter than 48 months.

FIG. 9 is a block flow diagram illustrating a methodology 107 forestimating unexpected residual lease losses. In accord with a preferredembodiment of the present invention, the methodology comprises compilinga historical portfolio of fully liquidated leasing contracts, post-leaseauction values and used-car price fluctuation data as illustrated inblock 110, forecasting auction price volatility as illustrated in block111 and adjusting the model to reflect used car price fluctuation (tocompensate for limited historical data) as illustrated in block 112.

Forecasting auction price volatility comprises quantifying auction pricefluctuations due to unknown and unpredictable factors. Accordingly,auction price variations due to seasonality or vehicle refreshenings arenot treated as part of volatility. To forecast auction volatility,economic models are applied to the historical profile for factoring outauction price variations due to seasonality and refreshenings.

Unexplained errors associated with the econometric models measure theauction price volatility. A 99.9% confidence lower bounds for thevolatility forecast is calculated according to the worst auction pricerealizations. Depreciation curves are implemented to calculateadditional amount of depreciation from a 24-month term to a 36-monthterm. In addition, 100% return rates are assumed for everynon-defaulting vehicle. Accordingly, it is assumed that everynon-defaulting vehicle is returned and experiences a worst-case residualloss.

FIG. 10 is a chart illustrating a comparison between actual past auctionvalues 115 as a percent of manufacturer suggested retail price (MSRP)116 and simulated past auction values 117 defined using the forecastingmodel discussed in FIG. 9. Notably, the forecasting model closelyreflects the actual values of the automobile auction market.

FIG. 11 illustrates the level that the forecasting model discussed inFIG. 10 missed actual unexpected residual loss values 120 and thefrequency 122 of each outcome. Notably, creditor protection 124 isprovided to the worst case with 99.9% confidence—a value significantlybeyond historical experience and three sigma (standard deviations) 126of the mean forecasted auction values 128.

FIG. 12 is a chart illustrating the frequency of residual lease losses135 and credit lease losses 136 as a function of loss severity 137. Inaccord with a preferred embodiment of the present invention, worst-caseresidual lease loss is calculated according to Equation 1.Lifetime residual lease losses=% of returned vehicles X loss severityEqn. 1

FIG. 13 is a chart illustrating unexpected residual lease losses basedthe difference between expected residual lease losses 145 and thesimulated worst-case residual lease loss 146 defined according toEquation 1.

FIG. 14 is a block flow diagram illustrating a methodology 147 forestimating unexpected commercial losses 62 in accord with the presentinvention. In accord with a preferred embodiment of the presentinvention, the methodology comprises compiling a historical portfolio ofautomobile dealer defaults as illustrated in block 150, applying asimulation model to assess the worst case scenario for the commercialloan portfolio, as illustrated in block 151 and calculating theunexpected commercial losses based on the simulation as illustrated inblock 152.

Preferably, the historical portfolio of automobile dealer defaultscomprises dealer concentration data, severity of default data, loan typedata (e.g., floor plan loans that are fully secured by new vehicles,dealer mortgage loans, dealer capital loans, commercial leasing andrentals) and probability of payment data. For default rates amongdealers, the historical data is analyzed to determine the defaultfrequency of the worst year as the worst-case scenario in the unexpectedcommercial loss calculation.

Preferably, the simulation model is run 200,000 times resulting in thedistribution of potential commercial losses. Using a conservativeapproach, the simulation model assumes that the worst-case default ratewill continue for five years. For example, a 1-year dealer default rateof 1% yields a 5% 5-year dealer default rate. Preferably, the assumedlosses are based on dealer commitment levels, not outstanding balances.

The methodology for calculating securitization losses 63 assumes thatunexpected securitization losses are consistent with those of on-balancesheet assets by product. The securitization methodology also treatsloans as on-balance sheet unless a true risk transfer has been achieved(i.e., the sale of tranches with a lower rating than the manufacturer'spresent rating or the sale of whole loans).

In accord with a preferred embodiment of the present invention, theunexpected securitization losses from each asset pool (e.g., retail,floor plan and lease) are calculated by multiplying the asset poolbalances by their respective unexpected loss percentages. Any transferof risk is subtracted from the total of unexpected securitization lossesfrom each asset pool.

The methodologies for calculating unexpected credit and residual lossesin foreign markets 64 is consistent with the respective credit andresidual methodologies described supra.

Miscellaneous assets 65 comprise assets on the manufacturer's balancesheet that have not been included in the retail 60, lease 61, commercial62, securitization 63 and foreign market 64 unexpected loss analysis.For example, miscellaneous assets 65 might include vehicles in theauction pipeline, prepaid insurance, cash & securities, deferredcharges, parts & accessories, good will and property & equipment.Unexpected losses for vehicles in the auction pipeline are calculatedbased on the lease methodology described supra. Unexpected losses forthe remaining miscellaneous assets (i.e., those assets not material insize and risk) are calculated based on management's perception of therisk.

Non-product assets 66 primarily comprise interest rate risk and legalrisk. Interest rate risk reflects the joint probability of adverseinterest rate movements and the planned interest rate mismatch on aportion of the manufacturer's portfolio. A simulation model isimplemented to reflect the joint probability resulting in thedistribution of potential losses. From this distribution, theprobability of each level of losses occurring is estimated.

Legal risk reflects the first loss on potential lawsuits and fraud underthe manufacturer's current insurance policy based on management'sperception of the risk.

FIG. 15 is a Venn diagram 155 illustrating a fundamental assumption ofthe present invention: that the correlation of retail risk 160, leaserisk 161 and commercial risk 162 reduces unexpected loss. In accord withthe present invention, it is assumed that these risks are interrelatedand cannot occur simultaneously. For example, assume that a manufacturerexperiences worst-case credit losses. As the demand for new carsdecreases, the demand for used cars increases because the need fortransportation does not decline as much as new vehicle sales. Therefore,in a downturn, used car prices are unlikely to be at their worst level.

FIG. 16 is a table illustrating an integration of the correlationassumption into a hypothetical example unexpected loss analysis. Assumefor the hypothetical example that historical government data regardingbankruptcies, delinquencies and their interrelationship, yields anaverage correlation of 0.5. Applying the 0.5 correlation to theunexpected credit losses 165, the unexpected residual losses 167 and theother unexpected losses 169 in a simulation model yields a correlationbenefit 171. Subtracting the correlation benefit 171 from the unexpectedloss total 168 yields an adjusted unexpected loss total 170.

Comparing Sources of Creditor

Protection to Unexpected Losses

FIG. 17 is a chart illustrating the sources of creditor protection 175as compared to the uses of that protection 176 over the lifetime of thefinance company's products. In accord with the present invention,sources of creditor protection 175 comprise lifetime profits and taxes177, deferred taxes 178, reserves 179 and equity & minority interest180. Uses of creditor protection 176 comprise future protection beyondthe 99.9% confidence level 181, present protection beyond the 99.9%confidence level 182 and unexpected losses 183.

Lifetime profits and taxes 177 assume a conservative after-tax return onequity for non-defaulting contracts within the existing portfolio. Asillustrated, lifetime profits 177 ramp-up over the lifetime of theportfolio.

Unexpected losses 183 are shown at an increasing rate over the lifetimeof the manufacturer's products based on the assumption that allunexpected losses will not occur on day one. For example, residuallosses can only occur as leases mature. Lessees do not have the optionof returning a lease vehicle early without making the manufacturer wholeon the lease contract or defaulting.

Notably, FIG. 17 illustrates a comparison between sources and uses ofcreditor protection during a profitable fiscal year. In the event worstcases losses occur, no taxes will be due and no dividend payments willbe made. In accord with a preferred embodiment of the present invention,the funds originally planned to pay taxes and dividends are used toservice debt.

As FIG. 17 illustrates, the finance company can provide its creditorswith protection beyond the 99.9% confidence level 182. This protectionis supplemented with future protection beyond the 99.9% confidence level181, which is provided by the non-defaulting contracts in the existingportfolio.

While the best mode for carrying out the invention has been described indetail, those familiar with the art to which this invention relates willrecognize various alternative designs and embodiments for practicing theinvention as defined by the following claims.

1. A method for assessing an automotive finance company's equityadequacy comprising: quantifying the company's sources of creditorprotection wherein the sources comprise equity, reserves and netdeferred tax liability in the event of an overall loss; estimating thecompany's potential unexpected worst-case losses for each of a pluralityof exposures with 99.9% confidence; and comparing the company's creditorprotection to the company's potential unexpected worst-case losses todemonstrate the company's equity adequacy.
 2. The method of claim 1wherein the sources of creditor protection additionally comprise futuretax liability.
 3. The method of claim 1 wherein the sources of creditorprotection additionally comprise lifetime profits.
 4. The method ofclaim 1 wherein a simulation model is implemented to estimate thecompany's potential unexpected worst-case losses for each of a pluralityof exposures with 99.9% confidence.
 5. The method of claim 1 whereinpotential unexpected worst-case residual lease exposures are estimatedusing economic models to factor out historical auction price variationsdue to seasonality and refreshenings.
 6. The method of claim 5 whereinan assumption is made in the estimation of potential unexpected worstcase residual lease exposures that every non-defaulting lease vehicle isreturned and experiences a worst-case residual loss.
 7. The method ofclaim 1 wherein the sources of creditor protection comprises assetclasses junior to creditor claims.
 8. The method of claim 1 additionallycomprising applying a risk correlation value to the estimated unexpectedworst-case losses to yield a risk-adjusted unexpected loss estimate.